Modeling and Detection of Hydrodynamic Trends for Advancing Early-Tsunami Warnings
Zoheir Sabeur and Banafshe Arbab-Zavar IT Innovation Centre, Faculty of Physical and Applied Sciences, University of Southampton
Southampton, Hampshire, United Kingdom
Joachim Wächter, Martin Hammitzsch, Peter Löwe and Matthias Lendholdt Helmholtz-Zentrum Potsdam, Deutsches GeoForschungsZentrum, Postdam, Germany
Alberto Armigliato, Gianluca Pagnoni and Stefano Tinti Department of Physics, University of Bologna, Italy
Rachid Omira Department of Seismology and Geophysics, the Meteorological Institute, Lisbon, Portugal
ABSTRACT
The automated detection of tsunamigenic signals at oceanic observation
stations is highly desirable for the advancement of current tsunami
early warning systems. These are supported with matching methods
using large numbers of tsunami wave propagation modeling scenarios.
New techniques using real-time scanning of hydrodynamic signals
around a network of stations in an open ocean have been developed for
the detection of tsunamis. Spectral ratios with respect to background
signals and their levels of similarity across stations were investigated.
The new developed algorithms will be wrapped as a reporting web
service for the TRIDEC tsunami early warning system in the future.
KEY WORDS: Tsunami wave propagation models; tsunamigenic
signal detection; data fusion; tsunami spectral analysis; tsunami signal
re-identification; TRIDEC system of systems; Open Geospatial
standards.
INTRODUCTION
The rapid development of information communication and sensing
technologies over the last decade has led various research programmes
to be launched for building the next generation information decision-
support systems which specialise in large scale environmental crises
management with advanced situation awareness. In the TRIDEC
research programme of Collaborative, Complex and Critical Decision-
Support in Evolving Crises (Sabeur, et al, 2011) one focuses on the
development of software architecture of open interoperable services
that support the intelligent management of large volumes of data from
heterogeneous sources (Moßgraber et al, 2012). The efficient handling
of large information and heterogeneous data is of paramount
importance so that key knowledge for decision support is critically
extracted and delivered to decision-makers during evolving crises.
Specifically, it requires the deployment of a structured framework of
data fusion and modelling for the management of data and information,
while overcoming their increase in volumes, heterogeneity and inherent
semantic gaps. The fusion framework addresses: a) Data semantic
alignments; b) Data aggregation; c) Processing data for both tsunami
wave modelling scenarios and automated detection of tsunamigenic
patterns; and d) Prediction of trends with computed spatial levels of
confidence. The generated results from these respective levels of
intelligent data management is stored in a Knowledge Base and made
accessible on-demand under the TRIDEC system of systems (Sabeur, et
al, 2012a). The storage solutions for the Knowledge Base are an
inclusive part of the adopted intelligent information management
strategies for advancing large scale decision-support systems for natural
crisis management such as in TRIDEC. In this paper, a three-fold set of
complementary research work conducted by the TRIDEC consortium
of partners is described. It reflects upon the joint development required
for building the next generation of the tsunami early warning systems.
These include: a) Databases of tsunami wave propagation model
simulations; b) Automated detection of tsunamigenic signals and
reporting; and c) A Knowledge Base with improved situation
awareness for the TRIDEC system of Systems.
TSUNAMI WAVE PROPAGATION MODELLING
Tsunami Generation by Earthquakes
The theory regarding tsunami generation by earthquakes is based on the
hypothesis that the earthquake itself produces a significant vertical
displacement of the seafloor in a very large area and in a very short
time. Although horizontal displacements may provide a non-trivial
contribution to tsunami generation, especially in complex-topography
areas, it is believed that the largest effect on tsunami genesis is played
by the vertical displacement component. Another key point regards the
energy transfer from the crust to the ocean. Experiments and theory
agree on the fact that, when a fluid is affected by the vertical
displacement of a portion of the floor of the basin in which it is
contained, the fluid reacts instantaneously with a vertical movement
such that its free surface deformation resembles identically the
deformation of the floor. As first approximation, it is assumed that the
tsunami generation process is insensitive to the time history of the
earthquake rupture, since this happens over time scales (typically few
40
Proceedings of the Twenty-third (2013) International Offshore and Polar EngineeringAnchorage, Alaska, USA, June 30–July 5, 2013Copyright © 2013 by the International Society of Offshore and Polar Engineers (ISOPE)ISBN 978-1-880653-99–9 (Set); ISSN 1098-6189 (Set)
www.isope.org
seconds) that are much smaller than the typical periods involved in the
tsunami process (several minutes to tens of minutes). This hypothesis
may fail for very large magnitude earthquakes (Mw larger than 8.5 –
9.0). The earthquakes with the largest tsunamigenic potential are those
that are able to produce the largest vertical displacements over large
portions of the seafloor. Since both co-seismic displacements and the
area affected by the deformation are increasing functions of the seismic
moment (or of the magnitude), the tsunamigenic potential of an
earthquake increases with the seismic moment of the earthquake.
Moreover, it is straightforward that submarine earthquakes are more
effective in generating tsunamis than earthquakes occurring close to the
coast or inland. Furthermore, shallow-hypocenter events are more
tsunamigenic than those having a deep focus. Finally, earthquakes with
focal mechanisms producing prevalently vertical displacements
(normal, reverse, thrust) have larger potential than those with strike-slip
focal mechanisms; which typically produce predominantly horizontal
deformations. Tsunami catalogues indicate that the most devastating
tsunamis which occurred in history were generated in correspondence
with subduction zones (Polet and Kanamori, 2000; Gusiakov, 2005),
where the typical fault mechanism is thrusting and the coseismic
displacements can be larger than 10 m. For instance, the 26th December
2004 (Mw=9.3) earthquake in the Indian Ocean ruptured a portion of
the subduction zone offshore Sumatra longer than 1000 km. Several
authors, based on different types of analyses, found that the slip on the
fault was highly heterogeneous and that it was as large as, or even
larger than 20 m in at least two major asperity zones (e.g. Banerjee et
al., 2007). In the case of the recent 11th March 2011 Mw=9.0 Tohoku
earthquake, which produced a devastating tsunami and killed around
20,000 people along the Japanese coasts, the largest coseismic slip on
the fault was up to 40 m (e.g. Simons et al., 2011).
Numerical Modeling of Tsunamis
Based on the assumptions which were mentioned previously, the
computation of the tsunami initial condition reduces to computing the
vertical displacement of the seafloor that is induced by the earthquake.
The other condition that is necessary to solve the hydrodynamics
equations is the initial velocity field. Since it is assumed that the initial
energy of the tsunami is purely potential, by hypothesis the initial
kinetic energy is null and hence the initial velocities are also null.
Consequently, in order to define the initial tsunami condition, it suffices
to compute the seafloor coseismic vertical displacements, which in turn
can be computed from the on-fault slip distribution using the elastic
dislocation theory (e.g. Okada, 1992). The techniques and the codes
used for the numerical simulation of tsunamis have made significant
progress in the last years, especially due to the highly increased interest
in the tsunami research field following the 2004 Indian Ocean event.
The largest part of the numerical models is based on the “long wave” or
“shallow water” approximation that neglects the vertical component of
the water particles velocity while it considers the horizontal velocity as
uniform (or averaged) along the vertical axis. In this way, the
unknowns of the hydrodynamics equations reduce to three, i.e. the free
surface elevation and the two horizontal velocity components.
Therefore the equations to solve are, i.e. the continuity equation (or
mass conservation equation) and the two equations for momentum
conservation. In order to produce the results presented here, we made
use of an inviscid, non-dispersive, non-linear shallow-water model
implemented into a finite-differences code (UBO-TSUFD), developed
and maintained by the Tsunami Research Team (TRT) at the
Department of Physics and Astronomy of the University of Bologna
(Italy). The code has been tested through several benchmarks and has
already been employed in the study of historical and recent tsunamis
(e.g. Tonini et al., 2011). The version we use solves the equations in
Cartesian coordinates over a set of nested, regularly spaced grids with
varying space resolution, allowing detailed results in selected areas.
Tsunami Scenarios for Portugal
The test area was on the western Iberia region; in TRIDEC, the
particular interest is on the impact of tsunami scenarios along the
southern coasts of Portugal. The scenario approach (see for instance
Tinti and Armigliato, 2003) is one of the two main approaches
traditionally used in tsunami hazard to risk assessment practices, the
other one being based on probabilistic/statistical analyses. The main
underlying idea is to select what is usually called a “worst-case
scenario”, which in turn is chosen on the basis of historical, tectonic
and geological/geomorphological considerations. Following this
approach, five different earthquake sources have been selected near
western Iberia (Omira et al, 2009). Here we adopted only one of these
faults, namely the Horseshoe Fault (HSF), which has been responsible
for some moderate-to-large magnitude earthquakes in the recent past
(28th February 1969, Mw=7.8; 12th February 2007, Mw=6.0). It has also
been considered by many authors as one of the possible responsible
faults for the great Lisbon earthquake which occurred on 1st November
1755 (Stich et al., 2007).
Fig.1. Upper panel: Heterogeneous slip distribution on a simplified
geometry for the Horseshoe fault (HSF). Lower panel: vertical
component of the seafloor displacement induced by the prescribed
rupture. Positive and negative displacements indicate uplift and
subsidence, respectively.
Fig. 1 shows the geometry of the fault, which is modelled as a 165 km
long and 70 km wide rectangle, with pure thrust focal mechanism and
dipping at 35° to the South-East. The geometry and the adopted
average slip (10.7 m) are compatible with a moment magnitude
Mw=8.3. The rectangle has been discretised into a matrix of 25x10 sub-
faults with the aim of introducing a (purely hypothetical) heterogeneous
slip distribution, whose pattern is shown in the upper panel of Fig. 1.
Each subfault is characterised by a uniform slip. By means of the
Okada (1992) model we computed the seafloor vertical deformation
induced by the prescribed slip distribution on the fault, and resulting in
the displacement field illustrated in the lower panel of Fig. 1. This
coincides with the tsunami initial condition. We simulated the ensuing
41
tsunami by means of the UBO-TSUFD code. As mentioned earlier, the
code can make use of nested grids: in this particular application, we are
interested in obtaining the best spatial resolution in correspondence
with five coastal places where sea-level monitoring sensors of the
Portuguese network are installed. The nesting configuration, shown in
the lower right panel of Fig. 2, involves a master 1-km resolution grid
(the larger domain) including two 200-m resolution grids: the first
covers the largest part of the Portuguese coastal area, while the second
covers the Madeira island area. In turn, the first 200-m grid includes
four 40-m resolution grids including the harbours of Cascais, Sesimbra,
Sines and Lagos where tide gauge sensors are installed. Similarly, a 40-
m grid covering the Funchal installation is nested into the second 200-
m grid. Overall, 8 grids have been used for modelling the tsunami wave
propagation scenarios.
Fig.2. Tsunami propagation fields computed at 10, 30 and 60 minutes
after the earthquake onset, and maximum water elevation (lower right
picture) after four hours. The limits of the computational grids (cyan
rectangles) have also been indicated in the lower left panel: Ca-Cascais,
Se-Sesimbra, Si-Sines, La-Lagos, Fu-Funchal.
Fig. 2 shows some of the possible outputs of UBO-TSUFD. Three
snapshots of the time evolution of the tsunami waves are displayed
respectively at 10 min, 30 min and 1 hour after the earthquake onset,
which in our approximation coincides with the tsunami generation. The
pattern of propagation is rather complicated and is mainly determined
by the bathymetry of the seafloor, the coastal morphology and the
geometry of the fault. The latter determines the preferential direction
with which the tsunami energy propagates from the source. In the HSF
case which we considered, two main fronts are seen to propagate in
opposite directions in the panel relative to 10 min after the tsunami
generation: one towards North West and the other one towards South
East. Both are almost perpendicular to the strike of the fault. In the
following time frames we can appreciate the role of bathymetry and
coastal morphology, which induce a number of effects on the
propagating waves, including reflection, refraction and diffraction.
Recalling that tsunami waves travel much faster in deep waters than in
shallow waters, it is seen that, upon approaching the coastlines, the
tsunami fronts tend to squeeze and to increase their amplitudes. The
lower right panel of Fig. 2 shows the maximum wave elevation
computed in each grid cell after four hours from the tsunami
generation. The pattern of the field confirms the main observation
drawn previously. The preferential direction of tsunami energy
propagation is determined by the strike of the fault, while bathymetry is
responsible for secondary offshore “beams” and for the distribution of
the largest impacts along the coastlines.
The above modeling work represents the typical backbone approach for
the development of databases with rich content of tsunamic wave
propagation realisations under various types of earthquakes. These
usually support the depiction of the best matching scenario of tsunami
genesis under a detected earthquake event with a given epicenter
location. In the next section, we present a supporting approach which
specializes in the automated detection of tsunamigenic signals that are
extracted from multiple observations from a network of hydrodynamic
stations in an open ocean.
AUTOMATED DETECTION OF TSUNAMIGENIC
SIGNALS
Regular geophysical monitoring of seismic activity at or near seas and
oceans provides an opportunity for early tsunami warning by selecting
from a database of pre-computed tsunami scenarios in an attempt to
predict the waveform and the run-ups of the putative tsunami at
potentially affected coastal zones. With current improvements in the
speed of execution of these simulators, it may even be possible to use
the seismic source characteristics as the input to these simulators and
thus execute a more relevant simulation in real time. However, there is
still another hindering factor in that it remains difficult and challenging
to obtain the rupture parameters of the tsunamigenic earthquakes in real
time and simulate the tsunami propagation with high accuracy.
Therefore even with fast simulators a question remains on the relevance
of a certain simulated tsunami signal as compared to the real
hydrodynamic signal. An evaluation of the observed sea-level signal is
therefore beneficial. Furthermore, in some cases the seismic signal
might be absent, for example in the case of submarine landslides and
volcanic eruptions which may not have been preceded by an earthquake
(Gisler, 2009).
For an early tsunami warning, the analysis of sea-level data in deep
waters away from the shore is required. Indeed, if the tsunami is
detected at the shore, it is already too late to issue a warning. In deep
waters, tsunamis propagate with modest wave heights. It is only when
these waves enter shallow waters that by wave shoaling effect their
wave height is increased significantly. These relatively low amplitudes
of tsunami signals at deep waters along with frequent occurrence of
background signals and noise contribute to a generally low signal to
noise ratio for the tsunami signal; which in turn makes the detection of
this signal very challenging. Tidal components account for a significant
part of the hydrodynamic signal. Being deterministic in nature, they can
be easily estimated and filtered out at each location of interest. There
are, however, several other factors which are not as easily removable.
Shelf resonance and local resonant features may also interfere with the
tsunami signal (Rabinovich, 1997). Wind waves, swell and associated
infra-gravity (IG) waves can create significant background noise at
tsunami frequencies (Rabinovich and Stephenson, 2004). Furthermore,
the tsunami wave train is constantly modified by refraction by seafloor
features and reflection from continental margins.
Recently there has been an interest in automatic detection of tsunami
signal from sea-level data. Simple thresholding methods have been
used by Mofjeld (1997), and Rabinovich and Stephenson (2004).
Artificial neural networks based applications to this problem were also
42
investigated by Beltrami (2008). Bressan and Tinti (2011, 2012) used
the slope of sea-level signals to detect tsunami and tsunami-like signals
while they designed performance indicators to evaluate the results. In
these approaches, the sea-level time series signal is analyzed directly to
detect the existence of tsunami or tsunami-like fluctuations.
In addition to the above, the transform of signals and the further usage
of the spectral properties of signals is required. The spectral signature
of a tsunamigenic signal is representative of its source event and it is
preserved along its propagation path. This provides a unique
opportunity to re-identify a spatially propagating tsunami from the
spectral properties of the sea-level signal at different hydrodynamic
stations. In this proposed approach, the following potential advantages
are sought for decision-support: a) Improvement of detection
confidence; b) Increased sensitivity to weaker signals; and c)
Facilitating the prediction of resonance effects at costal zones.
Nevertheless, it should be noted that by definition the methods that use
the sea-level time series directly offer a more instant solution. In these,
only few consecutive readings of the sea-level are used to make
detections. Spectral analysis of the signals, on the other hand, requires a
larger portion of observation data. While, obviously, an instant solution
is desirable, the state-of-the-art instant solutions are simply impractical
for detecting weaker signals in open oceans. In such cases and if the
time constraints allow, a solution can be offered through automatic
analysis of spectral properties and detections of tsunami signatures. A
good balance can be achieved by combining the two approaches to
provide both instant and more detailed detections.
Spectral Properties of Tsunamigenic Signals
It is well-known that the spectra of tsunami signals from different
events recorded at the same location are similar, while the same
tsunami recorded at different locations can be strongly different. This
was first proposed by Omori (1902) and was later examined in many
other studies (e.g. Miller, 1972, Bressan and Tinti, 2011). The
properties of tsunami signals recorded at the coast are mainly
determined by resonant influence of local topography rather than by the
tsunami source characteristics. On the other hand, the tsunami signal at
the open ocean away from the effects of the local topography is
considered a valuable source for determining the source characteristics.
At times, analyzing an open ocean tsunami signal can provide finer
details, regarding the source event, than can be obtained from analyzing
the respective seismic data.
The periods nT of the tsunami signal are related to the dimension of the
source event W and the mean water depth in the source area H , and is
given by the relation:
gHn
WTn ~ , ,...2,1=n
where g is the acceleration due to gravity (e.g. Rabinovich, 1997).
Notwithstanding the small increase in the period of tsunami wave
constituents during long distance propagation (Munk, 1946), the period
of the tsunami constituents can be assumed to remain invariant all
along its spatial propagation path . Thus the energy spectrum pattern of
the tsunami signal which is representative of its source characteristics is
for the most part maintained. We will refer to this spectral pattern as the
“tsunami signature”. Rabinovich (1997) has shown that, even for the
coastal regions, where the resonance influences of the local topography
may conceal the spectral characteristics of a tsunami source, these
influences can be estimated and removed, and thereby the unique
spectral pattern of the tsunami can be revealed.
Detecting the Tsunamigenic Signals
Given that every tsunami signal has a signature which is representative
of its source characteristics and that this signature is maintained
through its propagation path, we propose a re-identification framework
in which a tsunamigenic signal is detected via scanning a network of
hydrodynamic stations with water levels sensing in an attempt to re-
identify the same signature as the tsunami wave propagates through this
network (see Sabeur et al., 2012b for an earlier brief description).
Thereby the probability of erroneous detections due to low signal to
noise ratio is reduced and therefore higher confidence levels are
achieved. As well as supporting the initial detection and improving the
confidence of detection, a re-identified signal is indicative of the spatial
range of the signal, and thus it can be used to facilitate the identification
of certain background signals such as wind waves which generally do
not have as large a spatial reach as tsunamis. This approach for
signature matching can also be potentially used for a more reliable
matching with a pre-computed tsunami dataset while it can also be used
to predict resonance effects at the shore where the costal oscillation
patterns are known, as well as providing insights as to the tsunami
wave propagation path.
A scan of a set of hydrodynamic stations will be made synchronously,
utilizing a set of already detected putative tsunami signal/s for the re-
identification of the same tsunamigenic signal/s. In this, the Power
Spectrum Density (PSD) ratio with respect to the background, as was
described in (Rabinovich, 1997), is computed at each station in real-
time and is firstly assessed for any significant oscillations that are
different from the background and further compared against the
putative tsunami signature/s which have been previously detected at
any of these stations in an attempt to re-identify a propagating tsunami
wave. To obtain the PSD ratio of a segment of the hydrodynamic
signal, the tidal components of the signal are first removed using the
previously estimated tidal components at that location and the signal is
also de-trended. The spectral ratio is computed by taking the ratios of
the PSD values of this signal and the background signal at the
corresponding frequencies. When analyzing the spectral ratio of the
current segment at a station, two main questions are asked: i) Does the
signal include any oscillations which are significantly different from
the background? ii) Does the signal have a spectral signature similar to
any of the previously detected signatures? If both answers are positive a
strong re-identification has been made. If the signal is significantly
different from the background but does not resemble anything that was
observed before, this is considered a new signature and a new putative
tsunami. This is certainly the case when we first start scanning since
there are no prior detections. Finally, a signal which is weak but has a
signature similar to what has been seen before presents one of the main
cases for which the proposed re-identification framework can be very
beneficial where a weak signal which would not have been otherwise
detected is identified.
Experiments
In our initial test, we have investigated a set of deep-ocean bottom
pressure gauges (BPR) in the Pacific Ocean for the Chilean tsunami of
27th of February 2010. This data is provided by the Deep-ocean
Assessment and Reporting of Tsunamis (DART) buoys network system
of the National Oceanic and Atmospheric Administration (NOAA)
(González et al., 1998). This tsunami was generated by an
8.8=wM earthquake off the Southern coast of Chile and was observed
across the Pacific Ocean. We have scanned five DART stations: 21413
(North West Pacific), 21414 (North Pacific), 51406 (Central Pacific),
51425 and 51426 (west Pacific). Fig. 3a shows the location of these
stations in the Pacific Ocean. We assume that we can receive the sea-
43
level readings from these stations in real-time. Clearly, at each station
the tsunami can be only observed when and if it arrives at that station.
Note that in order to be able to calculate spectral ratios we need to have
knowledge of the background oscillations at each station. Therefore a
t∆ time, which we refer to as the initialization time, in the beginning
of each observation is devoted to capturing the characteristics of the
background signal. Once this is performed we can proceed with the
scan phase. In a mock real-time scan of these stations. A record of each
detected putative tsunami signal which has been observed at any one of
these stations is retained and used as the reference list for the re-
identification purpose. Spectral ratios are compared using the
Manhattan distance of normalized spectral ratios. Normalization is
achieved using the decibel unit and the maximum PSD ratio value for
each signal as the reference value, to ensure that the signals are
compared based on their energy distribution rather than their energy
levels. Two threshold values are used: one to determine whether the
signal is significantly different from the background, and the other to
determine a match between the currently analyzed spectral ratio and
previously detected signature/s. For multiple detections of a signal the
mean spectral ratio is used as the signature for that detection.
The sampling frequency of these stations is 15 seconds. We analyze
12.8 hours segments of sea-level data at each step. This analysis is
repeated every 15 minutes, thus a sliding window with a 15 minutes
step size is used (Fig. 3b.). The computation time required to analyze
one segment at one station is of an order of deciseconds with an
Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz processor and 16GB of
RAM. The first 12.8 hours segment is used for the computation of
background PSDs. We start our scan at zero hour of the 23rd of
February 2010 and finish at midnight on 5th of March 2010.. Fig. 4
shows the spectral ratios (the tsunami signature) for this tsunami
detected automatically at the five stations using the method described
above. The detected spectral ratios present a trend in the spectral
distribution of energy for this tsunami which can be seen more clearly
in the grey graph line, which is the mean value of the detected
signatures. As well as the general trend which is similar between the
spectral ratios detected at different stations, the mean signal has
dominant peaks at periods 32 min, 14 min and 7 min. The two smaller
periods are in good agreement with the Rabinovich et al. (2012) work
which have reported dominant periods of 6-8 min and 15 min for this
tsunami from six deep-sea observations by BPRs located seaward from
the coasts of Vancouver Island and Washington State.
Fig.3a. DART stations and the epicenter of the 2010 Chilean tsunami.
Fig.3b. Synchronous scan of the five DART stations
Fig.4. The spectral ratio of the 2010 Chilean tsunami automatically
detected on five DART stations demonstrates a good similarity between
the spectral ratios at different stations.
The main detected matching signatures that are shown in Fig. 4 are one
of three signature sets that are detected during our 11 day scan of the
five DART stations, and it represents the most energetic signal. The
two other detected signatures one corresponds to seismic waves which
are generally characterized by a higher frequency profile and the other
to the tail of the tsunami signal. As the tsunami wave propagates it
becomes increasingly more complex due to various refractions and
reflections thus other patterns start to emerge. However, these signals
are generally less energetic than the initial tsunami wave.
In the next section, the TRIDEC tsunami early warning system is
introduced. While, it currently uses the matching scenario approach for
tsunami warning, it shall be further advanced with supporting new
intelligent information management modules under the TRIDEC
project. Among these new modules will be an alert service for
automated detection of tsunamigenic signatures, which is based on the
technique of signal analysis described above.
44
TRIDEC TSUNAMI EARLY WARNING SYSTEM
Following the Boxing Day Tsunami of 2004, research initially
addressed isolated activities to improve Tsunami Early Warning (TEW)
capabilities. Over time, the focus changed towards the establishment of
reference architectures using standardised interfaces and reusable
components. This allowed the porting of complete TEW systems
(TEWS) or components to other regions of the world or application for
natural crises in general (Wächter et al., 2012).
Within a standards-based TEWS, a situation picture component,
provided via a Graphical User Interface (GUI) serves as the central
point of information for the Officers on Duty (OOD) to guide them
through the TEWS workflow (Fig. 5). The situation picture is based on
spatial data such as thematic maps and sensor observations. Spatial data
are provided via Open Geospatial Consortium (OGC) Web Map
Service (WMS) and the OGC Web Feature Service (WFS). Tsunami
simulation information can also be accessed using a OGC Web
Processing Service (WPS) (Hammitzsch et al., 2012).
TEWS use incoming sensor data to predict the expected tsunami
propagation pattern for the current event. This prediction is used to
identify regions and critical infrastructure under threat. In the
following, information logistics components compose and distribute
warning messages for selected recipients like government agencies,
which will disseminate the information further towards the affected
communities.
Fig.5. Simplified workflow of a Tsunami EarlyWarning System
(Lendholt and Hammitzsch, 2011).
Tsunami Simulation
A core concept for Tsunami Early Warning Systems is the use of
Tsunami simulation products, based on bathymetry, topography and
incoming sensor observations. These parameters are used to infer the
likely Tsunami spreading origins, to predict the propagation pattern and
the overall spatial and temporal extent of the Tsunami.
Simulation products are based on point grids, with wide mesh sizes in
the open sea and decreasing mesh sizes closer to the shorelines. The
simulation results are three-dimensional data sets, which factor in local
tides to state expected water level deviations.
An important part of the Tsunami propagation prediction is the estimate
of arrival times and expected wave heights for affected coastal areas
(Behrens et al., 2010). Models, algorithms and software systems used to
produce Tsunami simulation products are a focus of current research.
Visualizing Tsunami Parameters
Previous research activities in the projects German Indonesian Tsunami
Early Warning System (GITEWS, www.gitews.de), Distant Early
Warning System (DEWS, www.dews-online.org) and Collaborative,
Complex and Critical Decision-Support in Evolving Crises (TRIDEC,
www.tridec-online.eu) identified the following simulation products as
suitable for visualization in a TEWS workflow:
(i) Maximum water levels show maximum wave heights at selected
locations within a defined observation timeframe (Fig. 6).
Fig. 6. Simulated maximum Tsunami water levels are mapped as
discrete grey levels (left), combined with isolines (centre) and solely as
isolines (right). (Lendholt et al., 2012).
(ii) Isochrones map the advancing of the Tsunami wave front. This
can be combined with other information, including maximum water
levels (Fig. 7). Since isochrones can be derived for various parameters,
the observed variable has to be explicitly stated (e.g. first wave front or
other).
Fig.7. Isochrones for the estimated arrival time (ETA) of a Tsunami
simulation shown as continuous grey values (left), as isolines (center)
and as a composite (right), consiting of isochrones (light grey),
isohypses (black) and ETA-isochrones. (Lendholt et al., 2012).
(iii) Mareograms show sea level time-series for selected locations, e.g.
tide gauge sensor positions (Fig. 8). Combined with incoming
observation data mareograms can be used to manually assess
simulation validity and information value.
Fig.8. Mareograms for selected coastal locations of the Japanese coast
displaying water level changes over time, shown as a table (top left)
and plot (bottom right). (Lendholt et al., 2012).
(iv) Worst-Case-Estimates contain coastal forecasts for selected
locations including the Estimated Time of Arrival (ETA) of the
Tsunami wave fronts and maximum Estimated Wave Heights (EWH).
This information allows for a simplistic yet effective threat
classification for affected coastal regions (Lendholt, 2011). All four
described visualisation products are accessed as vector data in the
TEWS environment, having been derived from raster-based simulation
data sets. They are complemented by a non-vector product.
(v) Tsunami-animations are used as dynamic overlays for static maps
to foster intuitive understanding of the spatio-temporal dynamics of the
Tsunami event. Within the TEWS workflow they are used for high-
level overviews of reduced spatial resolution or to show local effects
within a limited time interval. An alternative application is visual
quality assessments of Tsunami simulations, to ensure their spatial and
temporal consistency and validity. Within TRIDEC, several workflows
have been set up to generate animation products, including the GFZ
45
High Performance Compute (HPC) Cluster and GRASS GIS (Löwe et
al., 2011 and Löwe et al., 2012).
CONCLUSIONS
The collaborative research and development work which has been
pursued in the TRIDEC Integrated Project since September 2010 is
converging towards building the next generation of advanced and
integrated tsunami early warning system of systems. The scalable
TRIDEC architecture of open processing services and a rich database of
tsunami wave propagation realizations are being built for the
Mediterranean and North-East Atlantic zone. Furthermore, a powerful
approach based on continuous spatial scanning water level signals at
hydrodynamic stations using wave spectra analyses has been
successfully developed for the automated detection of tsunamis. This
has the potential of providing increased situation awareness with more
detailed reporting about the tsunami wave propagation in real-time and
storage in the TRIDEC Knowledge Base. Also, the use open standards
in the construction of the Tsunami early warning system components
gives it a great potential of interoperability and low cost for coupling it
with other existing early warning systems which operate in many other
parts of the world oceans.
ACKNOWLEDGEMENTS
This research work is supported by the European Commission under
the TRIDEC Integrated Project contract Number: FP7 258723. Also,
the contributing Authors from the University of Southampton IT
Innovation Centre are very grateful to NOAA for using their open
hydrodynamic observation data of the Pacific Ocean, in order to
achieve their development of the automated detection algorithms on
tsunamigenic signals in the TRIDEC project.
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